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cs.CR2026
ROSETTA: Efficient and Accurate Privacy-Preserving LLM Decoding via Hybrid CKKS/TFHE Evaluation
Jiangrui Yu, Baosheng Zhang, Liang Kong +5
Generative large language models (LLMs) have achieved state-of-the-art performance on many real-world tasks such as code generation and question answering. These models predominant…
cs.AR2026
OptiPrime: Optimizing Private Inference through Protocol-Hardware Co-design
Jiangrui Yu, Ye Yu, Si Chen +5
Private deep neural network (DNN) inference based on hybrid homomorphic encryption (HE) and multi-party computation (MPC) can protect user data with a formal guarantee, but at the…